The continued scaling of flash memory technology into smaller process nodes, combined with the increased information capacity of each flash cell (i.e, storing more bits per cell), has placed NAND flash memory at the forefront of modern storage technology.

Recent years have witnessed a significant increase in the storage density of NAND flash memory, making it a critical component in modern electronic devices. However, with the rise in storage capacity comes an increased likelihood of errors in data storage and retrieval. The growing number of errors poses ongoing challenges for system designers and engineers, in terms of the characterization, modeling, and optimization of NAND-based systems. We present a novel approach for modeling and preventing errors by utilizing the capabilities of generative and unsupervised machine learning methods. As part of our research, we constructed and trained a neural modulator that translates information bits into programming operations on each memory cell in NAND devices. Our modulator, tailored explicitly for flash memory channels, provides a smart writing scheme that reduces programming errors as well as compensates for data degradation over time. Specifically, the modulator is based on an auto-encoder architecture with an additional channel model embedded between the encoder and the decoder. A conditional generative adversarial network (cGAN) was used to construct the channel model. Optimized for the end-of-life work-point, the learned memory system outperforms the prior art by up to 56\% in raw bit error rate (RBER) and extends the lifetime of the flash memory block by up to 25\%.

Here, we propose a novel solution based on functionally-targeted stochastic modulation.

Neural Information Processing Systems http://nips.cc/

Continual Graph Learning(CGL)focuses on acquiring new knowledge while retaining previously learned information, essential for real-world graph applications. Current methods grapple with two main issues:1) The Stability-Plasticity Dilemma: Replay-based methods often create an imbalance between the Dilemma, while incurring significant storage costs.2) The Resource-Heavy Pre-training: Leading replay-free methods critically depend on extensively pre-trained backbones, this reliance imposes a substantial resource burden.In this paper, we argue that the key to overcoming these challenges lies not in replaying data or fine-tuning the entire network, but in dynamically modulating the internal computational flow of a frozen backbone. We posit that lightweight, task-specific modules can effectively steer a GNN's reasoning process. Motivated by this insight, we propose Task-Aware Adaptive Modulation(TAAM), a replay-free, resource-efficient approach that charts a new path for navigating the stability-plasticity dilemma. TAAM's core is its Neural Synapse Modulators(NSM), which are trained and then frozen for each task to store expert knowledge. A pivotal prototype-guided strategy governs these modulators: 1) For training, it initializes a new NSM by deep-copying from a similar past modulator to boost knowledge transfer. 2) For inference, it selects the most relevant frozen NSM for each task. These NSMs insert into a frozen GNN backbone to perform fine-grained, node-attentive modulation of its internal flow-different from the static perturbations of prior methods. Extensive experiments show that TAAM comprehensively outperforms state-of-the-art methods across six GCIL benchmark datasets. The code will be released upon acceptance of the paper.